The paper "Ill-Posed Problems in Early Vision" by M. Bertero, T. Poggio, and V. Torre reviews the mathematical aspects of ill-posed and ill-conditioned problems in early vision, a computational stage in vision systems that decodes 2D images into 3D surface properties. The authors discuss the challenges of inverse problems, such as motion recovery, shape from shading, and edge detection, which are often ill-posed or ill-conditioned. They introduce regularization theory, including linear and non-linear methods, and stochastic approaches based on Bayesian estimation. The paper also analyzes specific early vision problems, such as edge detection, optical flow, surface interpolation, and shape from shading, characterizing the existence, uniqueness, and stability of solutions. The goal is to provide a rigorous foundation for understanding and solving these complex problems in early vision.The paper "Ill-Posed Problems in Early Vision" by M. Bertero, T. Poggio, and V. Torre reviews the mathematical aspects of ill-posed and ill-conditioned problems in early vision, a computational stage in vision systems that decodes 2D images into 3D surface properties. The authors discuss the challenges of inverse problems, such as motion recovery, shape from shading, and edge detection, which are often ill-posed or ill-conditioned. They introduce regularization theory, including linear and non-linear methods, and stochastic approaches based on Bayesian estimation. The paper also analyzes specific early vision problems, such as edge detection, optical flow, surface interpolation, and shape from shading, characterizing the existence, uniqueness, and stability of solutions. The goal is to provide a rigorous foundation for understanding and solving these complex problems in early vision.